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Record W2982035234 · doi:10.1177/0192512119868323

Feminist reflections on discourses of (power) + (sharing) in power-sharing theory

2019· article· en· W2982035234 on OpenAlexaff
Siobhan Byrne

Bibliographic record

VenueInternational Political Science Review · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIntersectionalityPower (physics)SociologyEmpowermentInclusion (mineral)Corporate governanceDilemmaFeminist theoryPublic relationsFeminismEpistemologyPolitical scienceGender studiesManagementLawEconomics

Abstract

fetched live from OpenAlex

A recent call by some feminist conflict mediation practitioners proposes to rename power-sharing: either by prioritizing sharing over power or by replacing ‘power’ with the word ‘responsibility’. The purpose of these discursive reformulations is to move beyond just adding women to power-sharing institutions; instead, these proposals signal a desire to promote inclusion through a feminist emphasis on sharing in power-sharing systems above a masculinist emphasis on power. Inspired by these proposals and reflecting on the experiences of gender mediation experts, I work through critical feminist theories of intersectionality and feminist empowerment to show how power-sharing theory can be reimagined so that power is not just understood as coercive or as a finite resource that can only be divided between a limited number of privileged groups; rather, power can also be productive, as well as a central feature of all hierarchical relationships. I also explore how a feminist care ethic can offer alternative ways of conceiving of sharing in governance. My objective is to demonstrate how feminist approaches can provide a new language of both power and sharing to illuminate pathways through the ‘exclusion amid inclusion’ dilemma in power-sharing theory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.012
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0110.119
Scholarly communication0.0120.018
Open science0.0020.009
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.474
Teacher spread0.399 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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